Komparativna analiza algoritama grupiranja primjerenih obradi velikih skupova podataka

master's thesis

• Title (english): Comparative Analysis Of Clustering Algorithms Applicable To Massive Dataset Processing
• Author: Marko Šmitran
• Mentor: Goran Delač (mentor)
• Committee member: Goran Delač (predsjednik povjerenstva)
• Committee member: Marin Šilić (član povjerenstva)
• Committee member: Siniša Srbljić (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2019-07-09, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

Cilj ovog rada je ispitivanje i usporadba svojstava algoritama grupiranja prikladnih
za grupiranje velikih skupova podataka. Algoritmi odabrani kao prikladni u tu svrhu
su: K-means, CLARANS, BIRCH i DENCLUE. Kvaliteta grupiranja je najvažniji
kriterij usporedbe algoritama. Kvaliteta grupiranja mjeri se unutarnjim i vanjskim validacijskim metrika. Objašnjen je rad svih odabranih algoritama i validacijske metrike
su definirane, a potom su provedena ispitivanja. Algoritmi grupiranja primjenjeni su
ponajprije nad sintetiˇckim skupovima podataka, ali i na dva stvarna skupa. Ispitan je
utjecaj oblika grupa, neravnoteže med¯u grupama i šuma u podacima na kvalitete grupiranja te skalabilnost algoritama. Algoritmi su primjenjeni i na stvarnim skupovima
te je ocijenjena njihova prikladnost.

Abstract (english)

The goal of this paper is testing and comparison of properties of clustering algorithms
applicable to massive dataset processing. Algorithms chosen as applicable for this
task are: K-means, CLARANS, BIRCH and DENCLUE. Quality of clustering is the
most important criterion of comparison between algorithms. Clustering quality is measured by internal and external validation metrics. Each chosen clustering algorithm
is explained and each used validation metrics are defined, then testing was conducted.
Clustering algorithms were primarily used on syntetic datasets, but also on two real
ones. The goal of the conducted tests was to discern sensitivity of clustering algorithms
to the shape of clusters, cluster imbalance and clustering of noisy data and their
impact on quality of clusterings. Scalability of clustering algorithms was also tested.
Algorithms were also used on real datasets and their applicability was discussed.

• Keywords: Algoritmi grupiranja; veliki skupovi podataka; validacija grupiranja; K-means; CLARANS; BIRCH; DENCLUE
• Keywords (english): Clustering algorithms; massive datasets; cluster validation; K-means; CLARANS; BIRCH; DENCLUE
• Language: croatian
• URN:NBN: urn:nbn:hr:168:926376
• Study programme: Title: Computing; Study programme type: university; Study level: graduate; Academic / professional title: magistar/magistra inženjer/inženjerka računarstva (magistar/magistra inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2020-01-07 22:27:30